{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'BUFSIZE',\n",
       " 'CLIP',\n",
       " 'DataSource',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'WRAP',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_builtins',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_typing',\n",
       " '_using_numpy2_behavior',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'expm1x',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'pro_bar_vip',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "您的token不对，请确认。",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[41], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m pro \u001b[38;5;241m=\u001b[39m ts\u001b[38;5;241m.\u001b[39mpro_api(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m*******此处替换自己的token****8c5a451771219e9e626be2bdf0d8e57c4d024374f7d91c56e4b88676\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# 拉取数据\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m df \u001b[38;5;241m=\u001b[39m pro\u001b[38;5;241m.\u001b[39mdaily(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mts_code\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m601939.SH\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrade_date\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      6\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart_date\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m20100101\u001b[39m,\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend_date\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m20241225\u001b[39m,\n\u001b[0;32m      8\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moffset\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlimit\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     10\u001b[0m }, fields\u001b[38;5;241m=\u001b[39m[\n\u001b[0;32m     11\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mts_code\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     12\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrade_date\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     13\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopen\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     14\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhigh\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlow\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     16\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclose\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     17\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpre_close\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     18\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchange\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     19\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpct_chg\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     20\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvol\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     21\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mamount\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     22\u001b[0m ])\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n",
      "File \u001b[1;32mc:\\Users\\86155\\anaconda3\\Lib\\site-packages\\tushare\\pro\\client.py:45\u001b[0m, in \u001b[0;36mDataApi.query\u001b[1;34m(self, api_name, fields, **kwargs)\u001b[0m\n\u001b[0;32m     43\u001b[0m result \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(res\u001b[38;5;241m.\u001b[39mtext)\n\u001b[0;32m     44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcode\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m---> 45\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmsg\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m     46\u001b[0m data \u001b[38;5;241m=\u001b[39m result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     47\u001b[0m columns \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfields\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "\u001b[1;31mException\u001b[0m: 您的token不对，请确认。"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('*******此处替换自己的token****8c5a451771219e9e626be2bdf0d8e57c4d024374f7d91c56e4b88676')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"601939.SH\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts # type: ignore\n",
    "# 初始化pro接口\n",
    "pro = ts.pro_api('8c5a451771219e9e626be2bdf0d8e57c4d024374f7d91c56e4b88676')\n",
    "\n",
    "# 拉取数据\n",
    "df = pro.stock_basic(**{\n",
    "    \"ts_code\": \"\",\n",
    "    \"name\": \"\",\n",
    "    \"exchange\": \"\",\n",
    "    \"market\": \"\",\n",
    "    \"is_hs\": \"\",\n",
    "    \"list_status\": \"\",\n",
    "    \"limit\": \"\",\n",
    "    \"offset\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"symbol\",\n",
    "    \"name\",\n",
    "    \"area\",\n",
    "    \"industry\",\n",
    "    \"cnspell\",\n",
    "    \"market\",\n",
    "    \"list_date\",\n",
    "    \"act_name\",\n",
    "    \"act_ent_type\"\n",
    "])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock601939.csv\")\n",
    "stockname='建设银行'\n",
    "stockcode='601939'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601939.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\86155\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0816 - val_loss: 0.0043\n",
      "Epoch 2/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0016 - val_loss: 6.5947e-04\n",
      "Epoch 3/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.4542e-04 - val_loss: 5.8542e-04\n",
      "Epoch 4/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.1463e-04 - val_loss: 5.7768e-04\n",
      "Epoch 5/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.9940e-04 - val_loss: 5.5887e-04\n",
      "Epoch 6/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.8969e-04 - val_loss: 5.4107e-04\n",
      "Epoch 7/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.8172e-04 - val_loss: 5.2645e-04\n",
      "Epoch 8/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.7380e-04 - val_loss: 5.1184e-04\n",
      "Epoch 9/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.6573e-04 - val_loss: 4.9729e-04\n",
      "Epoch 10/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.5736e-04 - val_loss: 4.8262e-04\n",
      "Epoch 11/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.4898e-04 - val_loss: 4.6927e-04\n",
      "Epoch 12/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.3978e-04 - val_loss: 4.5175e-04\n",
      "Epoch 13/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.2968e-04 - val_loss: 4.4112e-04\n",
      "Epoch 14/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.2095e-04 - val_loss: 4.3086e-04\n",
      "Epoch 15/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.1173e-04 - val_loss: 4.2166e-04\n",
      "Epoch 16/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.0236e-04 - val_loss: 4.1320e-04\n",
      "Epoch 17/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.9301e-04 - val_loss: 4.0470e-04\n",
      "Epoch 18/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.8367e-04 - val_loss: 3.9475e-04\n",
      "Epoch 19/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.7452e-04 - val_loss: 3.8376e-04\n",
      "Epoch 20/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.6603e-04 - val_loss: 3.7170e-04\n",
      "Epoch 21/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.5829e-04 - val_loss: 3.5966e-04\n",
      "Epoch 22/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.5156e-04 - val_loss: 3.4891e-04\n",
      "Epoch 23/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.4552e-04 - val_loss: 3.3902e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.4007e-04 - val_loss: 3.3030e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.3515e-04 - val_loss: 3.2286e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3.3045e-04 - val_loss: 3.1654e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.2617e-04 - val_loss: 3.1095e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.2224e-04 - val_loss: 3.0631e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.1826e-04 - val_loss: 3.0212e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.1450e-04 - val_loss: 2.9842e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.01085\n",
      "均方根误差: 0.10417\n",
      "平均绝对误差: 0.07436\n",
      "R2: 0.97279\n"
     ]
    }
   ],
   "source": [
    "# 安装 Tushare 库（如果还没有安装）\n",
    "# !pip install tushare\n",
    "\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import array\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "import tushare as ts  # 导入 Tushare 库\n",
    "\n",
    "# 设置随机种子\n",
    "np.random.seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 获取数据\n",
    "# 请确保你的API Key是有效的，并且不要在公共代码中暴露它\n",
    "pro = ts.pro_api('8c5a451771219e9e626be2bdf0d8e57c4d024374f7d91c56e4b88676')\n",
    "df = pro.daily(ts_code='601939.SH', start_date='20100101', end_date='20241225')\n",
    "df.to_csv(\"stock601939.csv\")\n",
    "\n",
    "# 读取并处理数据\n",
    "mydata = pd.read_csv(\"stock601939.csv\", parse_dates=[\"trade_date\"], index_col=\"trade_date\")[[\"open\", \"high\", \"low\", \"close\"]]\n",
    "mydata = mydata.sort_index(ascending=True)  # 确保数据按时间顺序排列\n",
    "\n",
    "# 绘制K线图\n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "candlestick2_ohlc(ax, opens=mydata['open'].values, highs=mydata['high'].values, lows=mydata['low'].values, closes=mydata['close'].values, width=0.5, colorup=\"r\", colordown=\"g\")\n",
    "\n",
    "# 添加均线\n",
    "mydata['5'] = mydata['close'].rolling(window=5).mean()\n",
    "mydata['10'] = mydata['close'].rolling(window=10).mean()\n",
    "\n",
    "plt.plot(mydata['5'].values, alpha=0.5, label='MA5')\n",
    "plt.plot(mydata['10'].values, alpha=0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=10)\n",
    "plt.title('K线图及均线')\n",
    "plt.xlabel('日期')\n",
    "plt.ylabel('价格')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 参数设置\n",
    "n_timestamp = 5\n",
    "n_epochs = 30\n",
    "\n",
    "# 数据准备\n",
    "training_set = mydata.iloc[:, 3:4].to_numpy()  # 使用 'close' 列\n",
    "test_set = mydata.iloc[-300:, 3:4].to_numpy()  # 使用最后300天作为测试集\n",
    "\n",
    "sc = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled = sc.transform(test_set)\n",
    "\n",
    "# 数据切分\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence) - n_timestamp):\n",
    "        seq_x, seq_y = sequence[i:i+n_timestamp], sequence[i+n_timestamp]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test, y_test = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
    "\n",
    "# 模型构建\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=50, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss='mean_squared_error')\n",
    "\n",
    "# 训练模型\n",
    "history = model.fit(X_train, y_train, batch_size=64, epochs=n_epochs, validation_data=(X_test, y_test), validation_freq=1)\n",
    "\n",
    "# 绘制损失曲线\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('训练和验证损失')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 预测\n",
    "predicted_stock_price = model.predict(X_test)\n",
    "predicted_stock_price = sc.inverse_transform(predicted_stock_price)\n",
    "real_stock_price = sc.inverse_transform(y_test)\n",
    "\n",
    "# 绘制预测结果\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(real_stock_price, color='red', label='实际股票价格')\n",
    "plt.plot(predicted_stock_price, color='blue', label='预测股票价格')\n",
    "plt.title('股票价格预测')\n",
    "plt.xlabel('时间')\n",
    "plt.ylabel('股票价格')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 计算评价指标\n",
    "MSE = mean_squared_error(real_stock_price, predicted_stock_price)\n",
    "RMSE = mean_squared_error(real_stock_price, predicted_stock_price) ** 0.5\n",
    "MAE = mean_absolute_error(real_stock_price, predicted_stock_price)\n",
    "R2 = r2_score(real_stock_price, predicted_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)\n",
    "\n",
    "\n",
    "均方误差: 0.01245\n",
    "均方根误差: 0.11233\n",
    "平均绝对误差: 0.08122\n",
    "R2: 0.96830"
   ]
  }
 ],
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